Data Scientists: Illuminate Your Patterns with Pictures

Senior Program Director, Product Marketing, Big Data Analytics

Scientific inquiry is all about finding non-obvious patterns in observational data. It’s no surprise that that is also the core of data science.

Patterns may be obvious to any sentient creature, or they may be deeply invisible - until we invent the conceptual or technological tools to bring them to the surface. The conceptual tools may be groundbreaking paradigm shifts, such the “thought experiment" that shaped Einstein’s insight into special relativity, or powerful new frameworks of visual notation, such as Feynman’s diagrams of subatomic particle interactions.

Patterns feel ghostly and unreal until we can actually see them, on some level, with our eyes. The chief technological tools are whatever scientists and engineers can use to bring these ghosts to light. In the realm of the subatomic, the magical inventions have been visualization technologies such as the cloud chamber and the scanning tunneling microscope (the latter was invented by IBM, by the way). 

Most real-world data science serves commercial interests, rather than pure science. But the restless search for deep patterns is no less critical in the business wars than among geniuses vying for Nobel Prizes. Today’s data scientists have two broad sets of pattern-sensing tools: advanced visualizations and statistical algorithms. No advanced analytic toolkit is complete without a best-of-breed library of them, with visualizations serving as the core interface at the heart of every step in the development, maintenance, and governance processes. You will find these complementary technologies - visualizations and algorithms - supported within IBM SPSS Modeler and in the complementary Big Data platforms, such as IBM Netezza AnalyticsIBM InfoSphere BigInsights, and IBM InfoSphere Streams, where data is stored and resource-hungry computations are performed.

Connecting the Dots: Data Mining and Predictive Analytics in Law Enforcement and Intelligence Analysis

Source:Police Chief Magazine

Colleen McCue, Ph.D., Program Manager, Crime Analysis Unit,and Colonel Andre’ Parker, Chief of Police, Richmond Police Department, Richmond, Virginia

Law enforcement organizations are challenged by a staggering increase in data on a daily basis. In fact, it has been estimated that the amount of data in the world doubles every 20 months. Every transaction, every event, every blip of electricity has the potential to generate data. The events of September 11, 2001, have only served to increase the flood of data while underscoring the critical importance of timely and complete exploitation of law enforcement data resources. The changing face of the war on terrorism and the challenge to connect the dots faces all law enforcement professionals, including those in the local arena.

Data mining tools, which were once reserved for large federal agencies and research centers, are now available to enhance decision making and analysis in the state and local law enforcement arena. Used extensively in the business community, the newer data mining tools do not require huge IT budgets, specialized personnel, or advanced training in statistics. Rather, these products are highly intuitive, relatively easy-to-use, PC-based, and very accessible to the law enforcement community.

The Richmond, Virginia, Police Department is using data mining and predictive analytics for a variety of law enforcement and intelligence applications, including tactical crime analysis, risk and threat assessment, behavioral analysis of violent crime, and proactive deployment strategies.

IBM SPSS  software for predictive analytics
Predictive analytics helps your organization anticipate change so that you can plan and carry out strategies that improve outcomes. By applying predictive analytics solutions to data you already have, your organization can uncover unexpected patterns and associations and develop models to guide front-line interactions. This means you can prevent high-value customers from leaving, sell additional services to current customers, develop successful products more efficiently, or identify and minimize fraud and risk. Predictive analytics gives you the knowledge to predict…and the power to act.

IBM SPSS software for predictive analytics

Predictive analytics helps your organization anticipate change so that you can plan and carry out strategies that improve outcomes. By applying predictive analytics solutions to data you already have, your organization can uncover unexpected patterns and associations and develop models to guide front-line interactions. This means you can prevent high-value customers from leaving, sell additional services to current customers, develop successful products more efficiently, or identify and minimize fraud and risk. Predictive analytics gives you the knowledge to predict…and the power to act.

Lots of business people are captivated by the idea of using predictive analytics, but not every organization has analytics gurus on staff. IBM plans to bridge that gap with IBM SPSS Decision Management software. “It used to be that if you’re trying to do what-if modeling, you had to turn to statisticians, but there are only so many analytics Jedi Knights,” said Jeff Jonas, chief scientist of the IBM Entity Analytics group. “This new platform enables millions of business analysts to do what used to require statisticians, so it’s going to open up a whole new range of things that organization can do for themselves.”

IBM’s Secret Upside for SPSS Acquisition – Smart Infrastructure | BNET Technology Blog
My colleague Michael Hickins was right when he said that IBM’s acquisition of SPSS helped it get its hands on a rapidly growing market. But the real upside is not in depriving SAP of an area of business intelligence that it might have been smart in wanting. The big win is that IBM gets to integrate what SPSS can do into what Big Blue knows best: infrastructure. In fact, you might say that IBM is creating a new category, smart infrastructure.

IBM’s Secret Upside for SPSS Acquisition – Smart Infrastructure | BNET Technology Blog

My colleague Michael Hickins was right when he said that IBM’s acquisition of SPSS helped it get its hands on a rapidly growing market. But the real upside is not in depriving SAP of an area of business intelligence that it might have been smart in wanting. The big win is that IBM gets to integrate what SPSS can do into what Big Blue knows best: infrastructure. In fact, you might say that IBM is creating a new category, smart infrastructure.

I think IBM’s acquisition of SPSS will mark a seminal moment in that company’s evolution, and that it will also accelerate ” perhaps even greatly accelerate ” the broader evolution of the IT industry from one fixated on boxes and code that run internal operations to one that’s focused on providing insight and expertise that helps customers grow. It’s going to force IT companies of all stripes to stop spending way too much of their time thinking about the transaction that occurs when they make a sale and instead begin concentrating on driving interactions for customers.

Bob Evans, TechWeb’s Senior Vice-President and Content Director

Business analytics breakthrough

Use your information for business advantage

A recent study by the IBM Institute for Business Value determined two out of three organizations surveyed recognize the value of business analytics and are moving to apply them for business value. Still, many continue to depend on personal experience and informed intuition to make decisions.

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Predictive Text Analytics Customers Now Have Access to Software as a Service (SaaS) Language Translation to Uncover Trends and Sentiments in Global Textual Data (via SPSS Inc. Partners with Language Weaver to Offer Translation Services on Demand)

Predictive Text Analytics Customers Now Have Access to Software as a Service (SaaS) Language Translation to Uncover Trends and Sentiments in Global Textual Data (via SPSS Inc. Partners with Language Weaver to Offer Translation Services on Demand)